Supervised learning linear priority dispatch rules for job-shop scheduling

Authors:
Helga Ingimundardottir;Thomas Philip Runarsson
Affiliations:
School of Engineering and Natural Sciences, University of Iceland, Iceland;School of Engineering and Natural Sciences, University of Iceland, Iceland
Venue:
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Year:
2011

Citing 8
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Abstract

This paper introduces a framework in which dispatching rules for job-shop scheduling problems are discovered by analysing the characteristics of optimal solutions. Training data is created via randomly generated job-shop problem instances and their corresponding optimal solution. Linear classification is applied in order to identify good choices from worse ones, at each dispatching time step, in a supervised learning fashion. The method is purely data-driven, thus less problem specific insights are needed from the human heuristic algorithm designer. Experimental studies show that the learned linear priority dispatching rules outperforms common single priority dispatching rules, with respect to minimum makespan.